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Background: Activation and regulation of androgen receptor (AR) signaling and the DNA damage response impact the prostate cancer (PCa) treatment modalities of androgen deprivation therapy (ADT) and radiotherapy. Here, we have evaluated a role for human single-strand binding protein 1 (hSSB1/NABP2) in modulation of the cellular response to androgens and ionizing radiation (IR). hSSB1 has defined roles in transcription and maintenance of genome stability, yet little is known about this protein in PCa. Methods: We correlated hSSB1 with measures of genomic instability across available PCa cases from The Cancer Genome Atlas (TCGA). Microarray and subsequent pathway and transcription factor enrichment analysis were performed on LNCaP and DU145 prostate cancer cells. Results: Our data demonstrate that hSSB1 expression in PCa correlates with measures of genomic instability including multigene signatures and genomic scars that are reflective of defects in the repair of DNA double-strand breaks via homologous recombination. In response to IR-induced DNA damage, we demonstrate that hSSB1 regulates cellular pathways that control cell cycle progression and the associated checkpoints. In keeping with a role for hSSB1 in transcription, our analysis revealed that hSSB1 negatively modulates p53 and RNA polymerase II transcription in PCa. Of relevance to PCa pathology, our findings highlight a transcriptional role for hSSB1 in regulating the androgen response. We identified that AR function is predicted to be impacted by hSSB1 depletion, whereby this protein is required to modulate AR gene activity in PCa. Conclusions: Our findings point to a key role for hSSB1 in mediating the cellular response to androgen and DNA damage via modulation of transcription. Exploiting hSSB1 in PCa might yield benefits as a strategy to ensure a durable response to ADT and/or radiotherapy and improved patient outcomes. Refereed/Peer-reviewed

Purpose: To determine whether the classification-based machine learning (ML) or artificially intelligent (AI) techniques can predict distant recurrence flags (yes or no) in invasive breast cancer patients using the data comprising several clinicopathological measurements such as pathological staging of tumor and surrounding nodes deemed both pre and post neoadjuvant (i.e. either chemo, radiation or hormone based) therapy including imaging based therapy-response as well and the status of adjuvant therapy (Chemo or Anti-Her2/Neu antibody) therapy were administered to patients post resection of their tumor to minimize the possibility of diseases-recurrence in them. Method: The clinicopathological data from a retrospective study of 900 breast cancer patients at Duke university (posted at The Cancer Imaging Archive, i.e. TCIA) was tapped for our study. These patients received neoadjuvant therapies such as Chemo-, Radiation or Endocrine Hormone based therapy, had their responses evaluated through imaging and pathological staging (both tumor and nodal), and received Adjuvant therapies (such as Chemo or Anti-Her2/Neu antibody therapy) post tumor resection. The patient entries pertaining to ungraded tumor responses from therapies or those labelled as ‘Not applicable (NA)’ for any of the aforementioned clinicopathological parameters were filtered out retaining 161 patient-entries which were further split into train and test sets in 90:10 ratio where 90% were used for training (ntrain=144) and 10% for testing (ntest=17) of several ML models. Classification based machine learning models such as Random Forest (RF), C-Support Vector Classification (SVC) and Supervised Neural Network (aka Multi-Layer Perceptron, i.e. MLP) were employed to train and test aforementioned clinicopathological parameters to predict distant recurrence flags or labels (i.e. Yes or No). The training of the model was conducted using ImaGene software. Out of the three aforementioned models, RF seemed to have performed best with grid search activated over tree-depth hyperparameter to predict distant recurrence flags in test patients (ntest=17) at AUC=1.0 (p0.75. Conclusion: Random Forest model trained using grid search through tree-depth hyperparameter can predict distant recurrence flags (Yes or No) in invasive breast cancer patients at AUC in the range of 0.75 1.0 across multiple institutions, thereby aiding portability of models and advancing the idea of multi-instutional ML/AI model validations in Invasive Breast Carcinoma subtype of Breast Cancer. Citation Format: Shrey S. Sukhadia, Kristen Muller, Adrienne Workman, Shivashankar Nagaraj, Olivier Gevaert. Predicting distant recurrences in invasive breast carcinoma patients using their clinicopathological profiles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB070.

Background & Aims Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress, and novel therapeutic response in PC to develop a biomarker-driven therapeutic strategy targeting DDR and replication stress in PC. Methods We interrogated the transcriptome, genome, proteome, and functional characteristics of 61 novel PC patient–derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient-derived xenografts and human PC organoids. Results Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors, including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, cosegregates with response to platinum (P < .001) and PARP inhibitor therapy (P < .001) in vitro and in vivo. We generated a novel signature of replication stress that predicts response to ATR (P < .018) and WEE1 inhibitor (P < .029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P < .001) but was not associated with DDR deficiency. Conclusions Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR-proficient PC and after platinum therapy., Graphical abstract

Pancreatic ductal adenocarcinoma (PDAC) can be divided into transcriptomic subtypes with two broad lineages referred to as classical (pancreatic) and squamous. We find that these two subtypes are driven by distinct metabolic phenotypes. Loss of genes that drive endodermal lineage specification, HNF4A and GATA6, switch metabolic profiles from classical (pancreatic) to predominantly squamous, with glycogen synthase kinase 3 beta (GSK3β) a key regulator of glycolysis. Pharmacological inhibition of GSK3β results in selective sensitivity in the squamous subtype; however, a subset of these squamous patient-derived cell lines (PDCLs) acquires rapid drug tolerance. Using chromatin accessibility maps, we demonstrate that the squamous subtype can be further classified using chromatin accessibility to predict responsiveness and tolerance to GSK3β inhibitors. Our findings demonstrate that distinct patterns of chromatin accessibility can be used to identify patient subgroups that are indistinguishable by gene expression profiles, highlighting the utility of chromatin-based biomarkers for patient selection in the treatment of PDAC.